Testing similarity between first-order intensities of spatial point processes. A comparative study
| Autor: | Isabel Fuentes-Santos, Jorge Mateu, Wenceslao González-Manteiga |
|---|---|
| Přispěvatelé: | Agencia Estatal de Investigación (España), European Commission, Generalitat Valenciana, Belgian Science Policy Office |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Statistics and Probability
Statistics::Theory 0211 other engineering and technologies environmental risk 02 engineering and technology Criminology Nonparametric inference Spatial distribution 01 natural sciences Point process Bootstrap calibration Task (project management) 010104 statistics & probability conditional intensity Similarity (network science) Environmental risk Conditional intensity nonparametric inference Statistics Statistics::Methodology 0101 mathematics Computer Science::Databases Mathematics 021103 operations research business.industry Pattern recognition First order bootstrap calibration Modeling and Simulation Inhomogeneous point process inhomogeneous point process Artificial intelligence business criminology |
| Zdroj: | Repositori Universitat Jaume I Universitat Jaume I |
| Popis: | 21 pages, 6 figures, 8 tables.-- This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License Testing whether two spatial point processes have the same spatial distribution is an important task that can be addressed from different perspectives. A Kolmogorov-Smirnov test with asymptotic calibration and a Cramer von Mises type test with bootstrap calibration have recently been developed to compare the first-order intensity of two observed patterns. Motivated by common practice in epidemiological studies, we introduce a regression test based on the relative risk function with two alternative bootstrap calibrations. This paper compares the performance of these nonparametric tests through both an intensive simulation study, and the application to wildfire and crime data. The three tests provide good calibrations of the null hypothesis for simulated Poisson and non-Poisson spatial point processes, but the Cramer von Mises and regression tests outperform the cost-efficient Kolmogorov-Smirnov test in terms of power. In the real data analysis we have seen that the Kolmogorov-Smirnov test does not detect differences between spatial point patterns when dealing with sparse data. In view of these results, it would be preferable using the Cramer von Mises or regression tests despite their higher computational demand This work has been supported by Projects MTM2016-78917-R and MTM2016-76969-P (AEI/FEDER, UE), grant AICO/2019/198 from Generalitat Valenciana, and IAP network StUDyS grant 3E120297 from the Belgian Science Policy |
| Databáze: | OpenAIRE |
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